PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
Peyton Young
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Establishes a comparison principle for two-population killed-particle HJB equations on decomposed state spaces of alive measures and cemetery masses, plus mean-field limit and particle convergence results.
citing papers explorer
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Persona-Trained Monte Carlo: Estimating Market-Outcome Distributions via Swarms of Persona-Conditioned Neural Policy Bots in a Limit Order Book
PTMC is a proposed Monte Carlo estimator that generates market-outcome distributions by simulating continuous double-auction interactions among persona-conditioned neural-policy bots whose heterogeneity is drawn from a learned distribution.
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Controlled McKean--Vlasov Contagion with State-Dependent Killing
Establishes a comparison principle for two-population killed-particle HJB equations on decomposed state spaces of alive measures and cemetery masses, plus mean-field limit and particle convergence results.